'''simple docstring'''




import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


__UpperCAmelCase					=logging.getLogger(__name__)

__UpperCAmelCase					=tf.data.AUTOTUNE






def __lowerCAmelCase					(				)      ->       Any:
    __lowerCamelCase            =    argparse.ArgumentParser(description='''Train a masked language model on TPU.'''							)
    parser.add_argument(
        '''--pretrained_model_config'''							,    type=UpperCamelCase__							,    default='''roberta-base'''							,    help='''The model config to use. Note that we don\'t copy the model\'s weights, only the config!'''							,    )
    parser.add_argument(
        '''--tokenizer'''							,    type=UpperCamelCase__							,    default='''unigram-tokenizer-wikitext'''							,    help='''The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.'''							,    )

    parser.add_argument(
        '''--per_replica_batch_size'''							,    type=UpperCamelCase__							,    default=8							,    help='''Batch size per TPU core.'''							,    )

    parser.add_argument(
        '''--no_tpu'''							,    action='''store_true'''							,    help='''If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.'''							,    )

    parser.add_argument(
        '''--tpu_name'''							,    type=UpperCamelCase__							,    help='''Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.'''							,    default='''local'''							,    )

    parser.add_argument(
        '''--tpu_zone'''							,    type=UpperCamelCase__							,    help='''Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'''							,    )

    parser.add_argument(
        '''--gcp_project'''							,    type=UpperCamelCase__							,    help='''Google cloud project name. Only used for non-Colab TPU nodes.'''							)

    parser.add_argument(
        '''--bfloat16'''							,    action='''store_true'''							,    help='''Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.'''							,    )

    parser.add_argument(
        '''--train_dataset'''							,    type=UpperCamelCase__							,    help='''Path to training dataset to load. If the path begins with `gs://`'''
        ''' then the dataset will be loaded from a Google Cloud Storage bucket.'''							,    )

    parser.add_argument(
        '''--shuffle_buffer_size'''							,    type=UpperCamelCase__							,    default=2**18							,    help='''Size of the shuffle buffer (in samples)'''							,    )

    parser.add_argument(
        '''--eval_dataset'''							,    type=UpperCamelCase__							,    help='''Path to evaluation dataset to load. If the path begins with `gs://`'''
        ''' then the dataset will be loaded from a Google Cloud Storage bucket.'''							,    )

    parser.add_argument(
        '''--num_epochs'''							,    type=UpperCamelCase__							,    default=1							,    help='''Number of epochs to train for.'''							,    )

    parser.add_argument(
        '''--learning_rate'''							,    type=UpperCamelCase__							,    default=1E-4							,    help='''Learning rate to use for training.'''							,    )

    parser.add_argument(
        '''--weight_decay_rate'''							,    type=UpperCamelCase__							,    default=1E-3							,    help='''Weight decay rate to use for training.'''							,    )

    parser.add_argument(
        '''--max_length'''							,    type=UpperCamelCase__							,    default=5_12							,    help='''Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'''							,    )

    parser.add_argument(
        '''--mlm_probability'''							,    type=UpperCamelCase__							,    default=0.1_5							,    help='''Fraction of tokens to mask during training.'''							,    )

    parser.add_argument('''--output_dir'''							,    type=UpperCamelCase__							,    required=UpperCamelCase__							,    help='''Path to save model checkpoints to.'''							)
    parser.add_argument('''--hub_model_id'''							,    type=UpperCamelCase__							,    help='''Model ID to upload to on the Hugging Face Hub.'''							)

    __lowerCamelCase            =    parser.parse_args()
    return args






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       str:
    try:
        if args.tpu_name:
            __lowerCamelCase            =    tf.distribute.cluster_resolver.TPUClusterResolver(
                args.tpu_name							,    zone=args.tpu_zone							,    project=args.gcp_project							)
        else:
            __lowerCamelCase            =    tf.distribute.cluster_resolver.TPUClusterResolver()
    except ValueError:
        raise RuntimeError(
            '''Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '''
            '''--gcp_project. When running on a TPU VM, use --tpu_name local.'''							)

    tf.config.experimental_connect_to_cluster(UpperCamelCase__							)
    tf.tpu.experimental.initialize_tpu_system(UpperCamelCase__							)

    return tpu






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Union[str, Any]:
    __lowerCamelCase            =    0
    for file in file_list:
        __lowerCamelCase            =    file.split('''/'''							)[-1]
        __lowerCamelCase            =    re.search(r'''-\d+-(\d+)\.tfrecord'''							,    UpperCamelCase__							).group(1							)
        __lowerCamelCase            =    int(UpperCamelCase__							)
        num_samples += sample_count

    return num_samples






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=None							)      ->       Optional[Any]:
    __lowerCamelCase            =    count_samples(UpperCamelCase__							)
    __lowerCamelCase            =    tf.data.Dataset.from_tensor_slices(UpperCamelCase__							)
    if shuffle:
        __lowerCamelCase            =    dataset.shuffle(len(UpperCamelCase__							)							)
    __lowerCamelCase            =    tf.data.TFRecordDataset(UpperCamelCase__							,    num_parallel_reads=UpperCamelCase__							)
    # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
    __lowerCamelCase            =    dataset.apply(tf.data.experimental.assert_cardinality(UpperCamelCase__							)							)
    __lowerCamelCase            =    dataset.map(UpperCamelCase__							,    num_parallel_calls=UpperCamelCase__							)
    if shuffle:
        assert shuffle_buffer_size is not None
        __lowerCamelCase            =    dataset.shuffle(args.shuffle_buffer_size							)
    __lowerCamelCase            =    dataset.batch(UpperCamelCase__							,    drop_remainder=UpperCamelCase__							)
    __lowerCamelCase            =    dataset.map(UpperCamelCase__							,    num_parallel_calls=UpperCamelCase__							)
    __lowerCamelCase            =    dataset.prefetch(UpperCamelCase__							)
    return dataset






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Any:
    if not args.no_tpu:
        __lowerCamelCase            =    initialize_tpu(UpperCamelCase__							)
        __lowerCamelCase            =    tf.distribute.TPUStrategy(UpperCamelCase__							)
    else:
        __lowerCamelCase            =    tf.distribute.OneDeviceStrategy(device='''/gpu:0'''							)

    if args.bfloataa:
        tf.keras.mixed_precision.set_global_policy('''mixed_bfloat16'''							)

    __lowerCamelCase            =    AutoTokenizer.from_pretrained(args.tokenizer							)
    __lowerCamelCase            =    AutoConfig.from_pretrained(args.pretrained_model_config							)
    __lowerCamelCase            =    tokenizer.vocab_size

    __lowerCamelCase            =    tf.io.gfile.glob(os.path.join(args.train_dataset							,    '''*.tfrecord'''							)							)
    if not training_records:
        raise ValueError(f"""No .tfrecord files found in {args.train_dataset}."""							)
    __lowerCamelCase            =    tf.io.gfile.glob(os.path.join(args.eval_dataset							,    '''*.tfrecord'''							)							)
    if not eval_records:
        raise ValueError(f"""No .tfrecord files found in {args.eval_dataset}."""							)

    __lowerCamelCase            =    count_samples(UpperCamelCase__							)

    __lowerCamelCase            =    num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
    __lowerCamelCase            =    steps_per_epoch * args.num_epochs

    with strategy.scope():
        __lowerCamelCase            =    TFAutoModelForMaskedLM.from_config(UpperCamelCase__							)
        model(model.dummy_inputs							)  # Pass some dummy inputs through the model to ensure all the weights are built
        __lowerCamelCase      ,    __lowerCamelCase            =    create_optimizer(
            num_train_steps=UpperCamelCase__							,    num_warmup_steps=total_train_steps // 20							,    init_lr=args.learning_rate							,    weight_decay_rate=args.weight_decay_rate							,    )

        # Transformers models compute the right loss for their task by default when labels are passed, and will
        # use this for training unless you specify your own loss function in compile().
        model.compile(optimizer=UpperCamelCase__							,    metrics=['''accuracy''']							)

    def decode_fn(UpperCamelCase__							):
        __lowerCamelCase            =    {
            '''input_ids''': tf.io.FixedLenFeature(dtype=tf.intaa							,    shape=(args.max_length,)							),
            '''attention_mask''': tf.io.FixedLenFeature(dtype=tf.intaa							,    shape=(args.max_length,)							),
        }
        return tf.io.parse_single_example(UpperCamelCase__							,    UpperCamelCase__							)

    # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
    # use their methods in our data pipeline.
    __lowerCamelCase            =    DataCollatorForLanguageModeling(
        tokenizer=UpperCamelCase__							,    mlm_probability=args.mlm_probability							,    mlm=UpperCamelCase__							,    return_tensors='''tf'''							)

    def mask_with_collator(UpperCamelCase__							):
        # TF really needs an isin() function
        __lowerCamelCase            =    (
            ~tf.cast(batch['''attention_mask''']							,    tf.bool							)
            | (batch['''input_ids'''] == tokenizer.cls_token_id)
            | (batch['''input_ids'''] == tokenizer.sep_token_id)
        )
        __lowerCamelCase      ,    __lowerCamelCase            =    data_collator.tf_mask_tokens(
            batch['''input_ids''']							,    vocab_size=len(UpperCamelCase__							)							,    mask_token_id=tokenizer.mask_token_id							,    special_tokens_mask=UpperCamelCase__							,    )
        return batch

    __lowerCamelCase            =    args.per_replica_batch_size * strategy.num_replicas_in_sync

    __lowerCamelCase            =    prepare_dataset(
        UpperCamelCase__							,    decode_fn=UpperCamelCase__							,    mask_fn=UpperCamelCase__							,    batch_size=UpperCamelCase__							,    shuffle=UpperCamelCase__							,    shuffle_buffer_size=args.shuffle_buffer_size							,    )

    __lowerCamelCase            =    prepare_dataset(
        UpperCamelCase__							,    decode_fn=UpperCamelCase__							,    mask_fn=UpperCamelCase__							,    batch_size=UpperCamelCase__							,    shuffle=UpperCamelCase__							,    )

    __lowerCamelCase            =    []
    if args.hub_model_id:
        callbacks.append(
            PushToHubCallback(output_dir=args.output_dir							,    hub_model_id=args.hub_model_id							,    tokenizer=UpperCamelCase__							)							)

    model.fit(
        UpperCamelCase__							,    validation_data=UpperCamelCase__							,    epochs=args.num_epochs							,    callbacks=UpperCamelCase__							,    )

    model.save_pretrained(args.output_dir							)


if __name__ == "__main__":
  __UpperCAmelCase					=parse_args()
  main(args)

'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							)      ->       bool:
    if not isinstance(UpperCamelCase__							,    UpperCamelCase__							):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''							)
    if len(UpperCamelCase__							) == 0:
        raise ValueError('''Input list must be a non empty list'''							)
    if len(UpperCamelCase__							) == 1:
        return True
    __lowerCamelCase            =    series[1] - series[0]
    for index in range(len(UpperCamelCase__							) - 1							):
        if series[index + 1] - series[index] != common_diff:
            return False
    return True






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       float:
    if not isinstance(UpperCamelCase__							,    UpperCamelCase__							):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''							)
    if len(UpperCamelCase__							) == 0:
        raise ValueError('''Input list must be a non empty list'''							)
    __lowerCamelCase            =    0
    for val in series:
        answer += val
    return answer / len(UpperCamelCase__							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
